1. Field of the Invention
The present invention relates to a method of and apparatus for detecting noise, and more particularly, to a method of and apparatus for detecting noise for voice recognition in a mobile device.
2. Description of the Related Art
As the performance of mobile devices has improved and a variety of services in a mobile environment have been generally provided, a more convenient interface instead of a button input method is being requested. One of the technologies being highlighted as a replacement for the button input method is voice recognition.
However, due to the diversity of environments for mobile device use, the voice recognition in a mobile device is more exposed to a variety of noise environments than personal computer (PC)-based voice recognition. In particular, scratch noise due to a terminal gripping method, spike noise, and noise input from a surrounding environment in the process of recognition have a critical influence on the performance of the recognition. Also, since the characteristic of this noise is variable, it is difficult to remove this noise even though conventional noise removing algorithms are applied.
The most generally used method among the conventional noise detection technologies is using a power/energy change. This method has an advantage of simplicity in implementation and operability with a few resources, but has many errors in terms of the performance. Another approach is a statistical method using Gaussian mixture model (hereinafter referred to as GMM).
In the power/energy based detection method, a power/energy value is calculated in units of frames from a voice signal input, and according to whether or not the power/energy value exceeds a threshold, a noise signal is detected. This approach has the advantage of the simplicity in implementation and operability with a few resources, but it is difficult to set a threshold that can be applied to all environments, and the performance is limited because noise is determined simply by the power/energy value.
Meanwhile, in the method using the GMM, the probability value of each model is calculated by using a voice signal being input in units of frames, and by using the probability value, it is determined which model a current frame is similar to. The statistical approach using the GMM shows a satisfactory performance even in detection of scratch noise having a low power/energy value, and has better performance than that of the power/energy-based noise detection method. However, the statistical method using the GMM includes many errors when signals of similar characteristics are detected.